AnswerThis: The Fastest Way to Do Research (Save Time!)

AI ethics research becomes faster and more structured when researchers use AnswerThis in a repeatable workflow: identify a specific research gap, pull recent and relevant sources, synthesize them into a literature review draft, then iterate with AI-assisted editing tied directly to the saved papers. The payoff is time saved at every stage—especially moving from a broad topic to a focused research question and a credible set of citations.

The process starts with gap-finding. For a demonstration topic—AI ethics—AnswerThis is prompted to “find gaps in research related to AI ethics” and suggest a research paper topic. The workflow uses a structured literature review mode, sets a minimum citation count (10), disables turbo mode for more precise results, and filters to literature from roughly the last five years by setting a start date near 2020. After a short wait, it returns an analysis highlighting multiple gaps across the field. In the example, one standout gap centers on the practical implementation of AI ethics principles inside organizations—specifically that ethics is often discussed in theory but not consistently translated into real organizational practices.

From that gap, AnswerThis generates research questions that can be used immediately. One example question becomes the anchor for the rest of the workflow: what specific AI ethics principles organizations prioritize during implementation efforts. Rather than treat this as a one-off search, the workflow encourages creating a new project for each stage (e.g., an “AI ethics final” project) so sources and outputs stay organized.

Next comes evidence gathering and comprehension. A second structured literature review query retrieves a literature map of the topic, with the instruction to read papers deeply—at least those that will form the foundation of the research. Researchers are guided to save useful papers into an AnswerThis library, which then becomes the basis for later steps. To speed understanding, AnswerThis offers a “chat with papers” feature: selected papers can be summarized together, key points highlighted, complex sections explained, and citations verified by jumping to the exact location in the PDF.

For managing many sources, AnswerThis includes “extract data,” which compiles key findings across selected papers into a table and can export to CSV for spreadsheet workflows. To expand the bibliography beyond the initial search, “Citation Maps” visualize citation networks for a chosen paper, surfacing highly cited and well-connected works (including classic foundational papers). Another search option allows natural-language queries instead of Boolean logic, generating multiple internal queries and returning a comprehensive overview that can be sorted by citations.

Finally, writing the literature review is done by constraining the draft to the curated library. The literature review generator can use “precise search” to mine relevant paragraphs from full PDFs (and researchers can upload PDFs when access is missing). The draft then moves into an editor where citations can be added from the library, numbering can be automatically reformatted, sections can be expanded or rephrased with custom prompts, and writing can continue with AI assistance that stays grounded in the saved sources. The end result is a draft literature review that is iterative, citation-aware, and tightly linked to the researcher’s chosen gap and question.